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2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)最新文献

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Multi-Objective Clustering Ensemble 多目标聚类集成
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.49
Katti Faceli, A. Carvalho, M. D. Souto
{"title":"Multi-Objective Clustering Ensemble","authors":"Katti Faceli, A. Carvalho, M. D. Souto","doi":"10.1109/HIS.2006.49","DOIUrl":"https://doi.org/10.1109/HIS.2006.49","url":null,"abstract":"In this paper, we present an algorithm for cluster analysis that provides a robust way to deal with datasets presenting different types of clusters and allows finding more than one structure in a dataset. Our approach is based on ideas from cluster ensembles and multi-objective clustering. We apply a Pareto-based multi-objective genetic algorithm with a special crossover operator. Such an operator combines a number of partitions obtained according to different clustering criteria. As a result, our approach generates a concise and stable set of partitions representing different trade-offs between two validation measures related to different clustering criteria.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127989579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 79
Genetic Programming with Incremental Learning for Grammatical Inference 遗传规划与语法推理的增量学习
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.29
Ernesto Rodrigues, H. S. Lopes
{"title":"Genetic Programming with Incremental Learning for Grammatical Inference","authors":"Ernesto Rodrigues, H. S. Lopes","doi":"10.1109/HIS.2006.29","DOIUrl":"https://doi.org/10.1109/HIS.2006.29","url":null,"abstract":"We present an evolutionary algorithm for the inference of context-free grammars from positive and negative examples. The algorithm is based on genetic programming and uses a local optimization operator that is capable of improving the learning task. Ordinary genetic operators are modified so as to bias the search. The system was evaluated using Tomita¿s language examples and results were compared with another similar approach. Results show that the proposed approach is promising and more robust than the other one.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125555379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
DNA Computing Model for the 0/1 Knapsack Problem 0/1背包问题的DNA计算模型
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.21
Aili Han
{"title":"DNA Computing Model for the 0/1 Knapsack Problem","authors":"Aili Han","doi":"10.1109/HIS.2006.21","DOIUrl":"https://doi.org/10.1109/HIS.2006.21","url":null,"abstract":"We have devised a DNA encoding method and a corresponding DNA algorithm for the 0/1 knapsack problem. Suppose that item set I={1,2 ... n}, profit set P={p_1,p_2,...,p_n}, weight set W={w_1,w_2,...,w_n}, and knapsack capacity is c. We use two DNA strands s_i1 and s_i2 to encode each item i, where the DNA strand s_i1 is with a length of wi whose center part is with a length of p_i, and the DNA strand s_i2 is the reverse complement of the center part of s_i1. For any two items i,j we add one DNA strand s_aij as an additional code, which is the reverse complement of the last part of s_i1 and the first part of s_j1. The proposed DNA encoding method is an improvement on the previous ones, and it provides further evidence for the ability of DNA computing to solve numerical optimization problems.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121437396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Combining Architectures for Temporal Learning in Neural-Symbolic Systems 神经符号系统中时间学习的组合架构
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.17
Rafael V. Borges, L. Lamb, A. Garcez
{"title":"Combining Architectures for Temporal Learning in Neural-Symbolic Systems","authors":"Rafael V. Borges, L. Lamb, A. Garcez","doi":"10.1109/HIS.2006.17","DOIUrl":"https://doi.org/10.1109/HIS.2006.17","url":null,"abstract":"We present a new approach to incorporate a temporal dimension into a hybrid system, by integrating a symbolic model and recurrent neural networks. This combination is supported by an algorithm to perform empirical learning. Further, the network is submitted to testbeds to analyse the influence of background knowledge insertion in the experiments and to validate the algorithm¿s learning capability. Finally, we show that the proposed architecture outperforms existing approaches to temporal learning in connectionist systems.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126807245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Image Processing by Using a Novel Neural Network Simulator 一种新型神经网络模拟器的图像处理
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.35
D. Durackova, Patricia R. Grega
{"title":"Image Processing by Using a Novel Neural Network Simulator","authors":"D. Durackova, Patricia R. Grega","doi":"10.1109/HIS.2006.35","DOIUrl":"https://doi.org/10.1109/HIS.2006.35","url":null,"abstract":"There are many cases, where the image processing is performed using cellular neural networks. First, for good pattern recognition, the design of the suitable network model is very important. Thus, our goal in this work was to develop the simulator for the universal cellular neural network design and on the base of this design to create the suitable neural network for the image processing. On the base of this network we have also designed some base circuits, which will be used for the future implementation of the neuron/network on the chip. The idea is to use it for a more complex chip, used as the silicon retina in the future.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124937467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
GNeurAge: An Evolutionary Agent-Based System for Classification Tasks GNeurAge:一个基于智能体的任务分类进化系统
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.30
D. F. D. Oliveira, A. Canuto, André M. C. Campos
{"title":"GNeurAge: An Evolutionary Agent-Based System for Classification Tasks","authors":"D. F. D. Oliveira, A. Canuto, André M. C. Campos","doi":"10.1109/HIS.2006.30","DOIUrl":"https://doi.org/10.1109/HIS.2006.30","url":null,"abstract":"The use of intelligent agents in the structure of multiclassifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system has presented good results in some centralized and distributed classification tasks. In this paper, an investigation of using evolutionary techniques in the functioning of the NeurAge (GNeurAge) is performed. In order to do this, we are going to use genetic algorithm in two different phases: in the choice of the initial classifier; and during the functioning of NeurAge (test phase).","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122401041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Local Parameters Particle Swarm Optimization 局部参数粒子群算法
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.43
Peter Tawdross, A. König
{"title":"Local Parameters Particle Swarm Optimization","authors":"Peter Tawdross, A. König","doi":"10.1109/HIS.2006.43","DOIUrl":"https://doi.org/10.1109/HIS.2006.43","url":null,"abstract":"Recently the particle swarm optimization (PSO) has been used in many engineering applications, which operate in dynamic environment and has proved its competitiveness over genetic algorithmin many natural number approaches. In the state of the art, it is assumed that all the particles have the same parameters, while in the real world; each individual has its own character, which means each particle has different parameters. In this paper, we study the feasibility and the behavior of local parameters for each particle in the PSO, and control the parameters by a simple algorithm. More advanced control algorithm can be applied to improve the search. Adjusting our PSO for different applications is easier as the swarm parameters are adjusted automatically for each particle. However, this modification of PSO can be applied for any type of PSO to improve it. As an example, we apply it to the hierarchical particle swarm optimization (HPSO). The results are obtained in static and dynamic environments. Local approach with a naive controller overcomes the other approaches in most of the cases.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131443485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
Extracting Symbolic Rules from Clustering of Gene Expression Data 从基因表达数据聚类中提取符号规则
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.26
Welbson S. Costa, Mateus S. de Assis, M. D. Souto
{"title":"Extracting Symbolic Rules from Clustering of Gene Expression Data","authors":"Welbson S. Costa, Mateus S. de Assis, M. D. Souto","doi":"10.1109/HIS.2006.26","DOIUrl":"https://doi.org/10.1109/HIS.2006.26","url":null,"abstract":"In the last few years, the increasing automation applied to Biology processes has led to a fast accumulation of im- portant biological data. The wide biological implications present in these data makes its analysis unsuitable via con- ventional computing. In this context, Machine Learning (ML) techniques have been showing very promising. One of the ML techniques for analyzing these data is cluster- ing methods. Experimental studies have shown that, often, clusters generated via such methods are biologically mean- ingful. However, in general, the interpretation of the bio- logical meaning of the clusters formed is a very complex task. Thus, this paper invests its efforts in the study of tech- niques that makes the interpretation of clusters formed by clustering techniques more straightforward. In order to do so, unsupervisedML techniques (clustering techniques) will be associated with supervised ML techniques (rule genera- tion). The goal is to generate symbolic structures, such as IF-THEN rules, which are more comprehensible for humans","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131817603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion 基于熵多样性准则的进化多目标优化模糊集成分类器设计
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.20
Y. Nojima, H. Ishibuchi
{"title":"Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion","authors":"Y. Nojima, H. Ishibuchi","doi":"10.1109/HIS.2006.20","DOIUrl":"https://doi.org/10.1109/HIS.2006.20","url":null,"abstract":"In this paper, we propose a multi-classifier coding scheme and an entropy-based diversity criterion in evolutionary multiobjective optimization algorithms for the design of fuzzy ensemble classifiers. In a multi-classifier coding scheme, an ensemble classifier is coded as an integer string. Each string is evaluated by using its accuracy and diversity. We use two accuracy criteria. One is the overall classification rate of the string as an ensemble classifier. The other is the average classification rate of component classifiers in the ensemble classifier. As a diversity criterion, we use the entropy of outputs from component classifiers in the ensemble classifier. We examine four formulations based on the above criteria through computational experiments on benchmark data sets in the UCI machine learning repository. The experimental results show the effectiveness of the multi-classifier coding scheme and the entropy-based diversity criterion.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130182937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
Co-Evolution: An Approach to Automatic Generation of Fuzzy Systems 协同进化:一种自动生成模糊系统的方法
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI: 10.1109/HIS.2006.16
A. Talon, H. Camargo
{"title":"Co-Evolution: An Approach to Automatic Generation of Fuzzy Systems","authors":"A. Talon, H. Camargo","doi":"10.1109/HIS.2006.16","DOIUrl":"https://doi.org/10.1109/HIS.2006.16","url":null,"abstract":"This work focus on the problem of automatic generation of fuzzy systems by means of evolutionary computation, specifically using the approach of coevolution. Co-evolution is based on the idea of modular modeling of the problem subcomponents. In this work the subcomponents are represented by species, which have a collaborative relation among them. The fuzzy system to be created performs fuzzy pattern classification. Basically, the environment is composed by four different species, which have a hierarchical collaboration both in the generation of the species and in the fitness determination of the individuals of these species. These species are organized in levels, where the contribution in the specie generation happens from the lowest to highest levels and the contribution in the fitness determination happens from the highest to lowest levels. The fitness calculation includes evaluations of rules compactness, what was demonstrated to improve the system interpretability.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128517814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
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